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Collaborative filtering for people to people recommendation in social networks
conference contribution
posted on 2023-05-23, 09:18 authored by Cai, X, Bain, M, Krzywicki, A, Wobcke, W, Kim, YS, Compton, P, Mahidadia, APredicting people other people may like has recently become an important task in many online social networks. Traditional collaborative filtering approaches are popular in recommender systems to effectively predict user preferences for items. However, in online social networks people have a dual role as both “users” and “items”, e.g., both initiating and receiving contacts. Here the assumption of active users and passive items in traditional collaborative filtering is inapplicable. In this paper we propose a model that fully captures the bilateral role of user interactions within a social network and formulate collaborative filtering methods to enable people to people recommendation. In this model users can be similar to other users in two ways – either having similar “taste” for the users they contact, or having similar “attractiveness” for the users who contact them.We develop SocialCollab, a novel neighbourbased collaborative filtering algorithm to predict, for a given user, other users they may like to contact, based on user similarity in terms of both attractiveness and taste. In social networks this goes beyond traditional, merely taste-based, collaborative filtering for item selection. Evaluation of the proposed recommender system on datasets from a commercial online social network show improvements over traditional collaborative filtering.
History
Publication title
Lecture Notes in Computer Science Volume 6464: AI 2010Volume
6464Editors
J LiPagination
476-485ISBN
978-3-642-17431-5Department/School
School of Information and Communication TechnologyPublisher
Springer-VerlagPlace of publication
GermanyEvent title
Advances in Artificial Intelligence 2010Event Venue
Adelaide, AustraliaDate of Event (Start Date)
2010-12-07Date of Event (End Date)
2010-12-10Rights statement
Copyright 2010 SpringerRepository Status
- Restricted